{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:01:49Z","timestamp":1792789309914,"version":"4.5.0"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Biotechnol"],"DOI":"10.1038\/s41587-026-03007-5","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T10:02:50Z","timestamp":1770372170000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Single-round evolution of RNA aptamers with GRAPE-LM"],"prefix":"10.1038","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0484-0966","authenticated-orcid":false,"given":"Jun","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6447-7085","authenticated-orcid":false,"given":"Ju","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoxuan","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuancheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yushan","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1959-9205","authenticated-orcid":false,"given":"Hao","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangjie","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3503-5161","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5188-4622","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"key":"3007_CR1","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1038\/nrd.2016.199","volume":"16","author":"J Zhou","year":"2017","unstructured":"Zhou, J. & Rossi, J. Aptamers as targeted therapeutics: current potential and challenges. Nat. Rev. Drug Discov. 16, 181\u2013202 (2017).","journal-title":"Nat. Rev. Drug Discov."},{"key":"3007_CR2","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.tig.2023.10.001","volume":"40","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Lang, M., Zhou, Y. & Zhang, Y. Predicting RNA structures and functions by artificial intelligence. Trends Genet. 40, 94\u2013107 (2024).","journal-title":"Trends Genet."},{"key":"3007_CR3","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1126\/science.ads0018","volume":"387","author":"T Hayes","year":"2025","unstructured":"Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850\u2013858 (2025).","journal-title":"Science"},{"key":"3007_CR4","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1038\/s41586-023-06127-z","volume":"621","author":"H Zhang","year":"2023","unstructured":"Zhang, H. et al. Algorithm for optimized mRNA design improves stability and immunogenicity. Nature 621, 396\u2013403 (2023).","journal-title":"Nature"},{"key":"3007_CR5","doi-asserted-by":"publisher","DOI":"10.1126\/science.adr6006","volume":"387","author":"K Jiang","year":"2025","unstructured":"Jiang, K. et al. Rapid in silico directed evolution by a protein language model with EVOLVEpro. Science 387, eadr6006 (2025).","journal-title":"Science"},{"key":"3007_CR6","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1038\/nrmicro1458","volume":"4","author":"DHJ Bunka","year":"2006","unstructured":"Bunka, D. H. J. & Stockley, P. G. Aptamers come of age \u2013 at last. Nat. Rev. Microbiol. 4, 588\u2013596 (2006).","journal-title":"Nat. Rev. Microbiol."},{"key":"3007_CR7","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.tibtech.2022.07.012","volume":"41","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Juhas, M. & Kwok, C. K. Aptamers targeting SARS-COV-2: a promising tool to fight against COVID-19. Trends Biotechnol. 41, 528\u2013544 (2023).","journal-title":"Trends Biotechnol."},{"key":"3007_CR8","doi-asserted-by":"publisher","first-page":"941","DOI":"10.3390\/molecules24050941","volume":"24","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Lai, B. S. & Juhas, M. Recent advances in aptamer discovery and applications. Molecules 24, 941 (2019).","journal-title":"Molecules"},{"key":"3007_CR9","doi-asserted-by":"publisher","first-page":"386","DOI":"10.3389\/fcimb.2019.00386","volume":"9","author":"M Vargas-Montes","year":"2019","unstructured":"Vargas-Montes, M. et al. Enzyme-linked aptamer assay (ELAA) for detection of toxoplasma ROP18 protein in human serum. Front. Cell. Infect. Microbiol. 9, 386 (2019).","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"3007_CR10","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1126\/science.2200121","volume":"249","author":"C Tuerk","year":"1990","unstructured":"Tuerk, C. & Gold, L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 249, 505\u2013510 (1990).","journal-title":"Science"},{"key":"3007_CR11","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202300656","volume":"10","author":"J Zhang","year":"2023","unstructured":"Zhang, J. et al. Repurposing CRISPR\/Cas to discover SARS-CoV-2 detecting and neutralizing aptamers. Adv. Sci. 10, 2300656 (2023).","journal-title":"Adv. Sci."},{"key":"3007_CR12","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-55957-0","volume":"16","author":"Q Su-Tobon","year":"2025","unstructured":"Su-Tobon, Q. et al. CRISPR-Hybrid: a CRISPR-mediated intracellular directed evolution platform for RNA aptamers. Nat. Commun. 16, 595 (2025).","journal-title":"Nat. Commun."},{"key":"3007_CR13","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1038\/s43588-022-00249-6","volume":"2","author":"N Iwano","year":"2022","unstructured":"Iwano, N., Adachi, T., Aoki, K., Nakamura, Y. & Hamada, M. Generative aptamer discovery using RaptGen. Nat. Comput. Sci. 2, 378\u2013386 (2022).","journal-title":"Nat. Comput. Sci."},{"key":"3007_CR14","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae517","volume":"25","author":"Z Wang","year":"2024","unstructured":"Wang, Z. et al. AptaDiff: de novo design and optimization of aptamers based on diffusion models. Brief. Bioinform. 25, bbae517 (2024).","journal-title":"Brief. Bioinform."},{"key":"3007_CR15","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1038\/s43588-024-00720-6","volume":"4","author":"F Wong","year":"2024","unstructured":"Wong, F. et al. Deep generative design of RNA aptamers using structural predictions. Nat. Comput. Sci. 4, 829\u2013839 (2024).","journal-title":"Nat. Comput. Sci."},{"key":"3007_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43166-6","volume":"14","author":"X Zhou","year":"2023","unstructured":"Zhou, X. et al. ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention. Nat. Commun. 14, 7434 (2023).","journal-title":"Nat. Commun."},{"key":"3007_CR17","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1126\/science.add2187","volume":"378","author":"J Dauparas","year":"2022","unstructured":"Dauparas, J. et al. Robust deep learning\u2013based protein sequence design using ProteinMPNN. Science 378, 49\u201356 (2022).","journal-title":"Science"},{"key":"3007_CR18","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1038\/s42256-024-00838-2","volume":"6","author":"M Ren","year":"2024","unstructured":"Ren, M., Yu, C., Bu, D. & Zhang, H. Accurate and robust protein sequence design with CarbonDesign. Nat. Mach. Intell. 6, 536\u2013547 (2024).","journal-title":"Nat. Mach. Intell."},{"key":"3007_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0253760","volume":"16","author":"G Lee","year":"2021","unstructured":"Lee, G., Jang, G. H., Kang, H. Y. & Song, G. Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach. PLoS ONE 16, e0253760 (2021).","journal-title":"PLoS ONE"},{"key":"3007_CR20","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-023-05577-6","volume":"24","author":"I Shin","year":"2023","unstructured":"Shin, I. et al. AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders. BMC Bioinformatics 24, 447 (2023).","journal-title":"BMC Bioinformatics"},{"key":"3007_CR21","unstructured":"Patel, S. et al. AptaBLE: an enhanced deep learning platform for aptamer protein interaction prediction and design. In Machine Learning for Structural Biology Workshop, NeurIPS 2024 https:\/\/www.mlsb.io\/papers_2024\/AptaBLE:_An_Enhanced_Deep_Learning_Platform_for_Aptamer_Protein_Interaction_Prediction_and_Design.pdf (2024)."},{"key":"3007_CR22","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","volume":"33","author":"B Alipanahi","year":"2015","unstructured":"Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831\u2013838 (2015).","journal-title":"Nat. Biotechnol."},{"key":"3007_CR23","doi-asserted-by":"publisher","first-page":"7465","DOI":"10.1093\/nar\/gkae541","volume":"52","author":"C Nithin","year":"2024","unstructured":"Nithin, C., Kmiecik, S., B\u0142aszczyk, R., Nowicka, J. & Tuszy\u0144ska, I. Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA\u2013ligand interactions. Nucleic Acids Res. 52, 7465\u20137486 (2024).","journal-title":"Nucleic Acids Res."},{"key":"3007_CR24","doi-asserted-by":"publisher","unstructured":"Chen, L. T. et al. Target sequence-conditioned design of peptide binders using masked language modeling. Nat. Biotechnol. https:\/\/doi.org\/10.1038\/s41587-025-02761-2 (2025).","DOI":"10.1038\/s41587-025-02761-2"},{"key":"3007_CR25","doi-asserted-by":"publisher","unstructured":"Torres, M. D. T., Chen, L. T., Wan, F., Chatterjee, P. & de la Fuente-Nunez, C. Generative latent diffusion language modeling yields anti-infective synthetic peptides. Cell Biomat. https:\/\/doi.org\/10.1016\/j.celbio.2025.100183 (2025).","DOI":"10.1016\/j.celbio.2025.100183"},{"key":"3007_CR26","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1038\/s41592-024-02487-0","volume":"21","author":"T Shen","year":"2024","unstructured":"Shen, T. et al. Accurate RNA 3D structure prediction using a language model-based deep learning approach. Nat. Methods 21, 2287\u20132298 (2024).","journal-title":"Nat. Methods"},{"key":"3007_CR27","doi-asserted-by":"publisher","first-page":"lqac012","DOI":"10.1093\/nargab\/lqac012","volume":"4","author":"M Akiyama","year":"2022","unstructured":"Akiyama, M. & Sakakibara, Y. Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning. NAR Genom. Bioinform. 4, lqac012 (2022).","journal-title":"NAR Genom. Bioinform."},{"key":"3007_CR28","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1038\/s42256-024-00836-4","volume":"6","author":"N Wang","year":"2024","unstructured":"Wang, N. et al. Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning. Nat. Mach. Intell. 6, 548\u2013557 (2024).","journal-title":"Nat. Mach. Intell."},{"key":"3007_CR29","doi-asserted-by":"publisher","unstructured":"Patel, S., Peng, F. Z., Fraser, K., Chatterjee, P. & Yao, S. EvoFlow-RNA: generating and representing non-coding RNA with a language model. Preprint at bioRxiv https:\/\/doi.org\/10.1101\/2025.02.25.639942 (2025).","DOI":"10.1101\/2025.02.25.639942"},{"key":"3007_CR30","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-60872-5","volume":"16","author":"RJ Peni\u0107","year":"2025","unstructured":"Peni\u0107, R. J., Vla\u0161i\u0107, T., Huber, R. G., Wan, Y. & \u0160iki\u0107, M. RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks. Nat. Commun. 16, 5671 (2025).","journal-title":"Nat. Commun."},{"key":"3007_CR31","doi-asserted-by":"publisher","DOI":"10.1126\/science.ado9336","volume":"386","author":"E Nguyen","year":"2024","unstructured":"Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).","journal-title":"Science"},{"key":"3007_CR32","doi-asserted-by":"publisher","first-page":"e82","DOI":"10.1093\/nar\/gkaa484","volume":"48","author":"R Ishida","year":"2020","unstructured":"Ishida, R. et al. RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information. Nucleic Acids Res. 48, e82 (2020).","journal-title":"Nucleic Acids Res."},{"key":"3007_CR33","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","volume":"22","author":"W Li","year":"2006","unstructured":"Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658\u20131659 (2006).","journal-title":"Bioinformatics"},{"key":"3007_CR34","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.omtn.2020.08.016","volume":"22","author":"L Freage","year":"2020","unstructured":"Freage, L., Jamal, D., Williams, N. B. & Mallikaratchy, P. R. A homodimeric aptamer variant generated from ligand-guided selection activates the T cell receptor cluster of differentiation 3 complex. Mol. Ther. Nucleic Acids 22, 167\u2013178 (2020).","journal-title":"Mol. Ther. Nucleic Acids"},{"key":"3007_CR35","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1021\/acs.biochem.9b00919","volume":"59","author":"H Zumrut","year":"2020","unstructured":"Zumrut, H. et al. Ligand-guided selection with artificially expanded genetic information systems against TCR-CD3\u03b5. Biochemistry 59, 552\u2013562 (2020).","journal-title":"Biochemistry"},{"key":"3007_CR36","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.omtn.2019.05.015","volume":"17","author":"HE Zumrut","year":"2019","unstructured":"Zumrut, H. E. et al. Integrating ligand-receptor interactions and in vitro evolution for streamlined discovery of artificial nucleic acid ligands. Mol. Ther. Nucleic Acids 17, 150\u2013163 (2019).","journal-title":"Mol. Ther. Nucleic Acids"},{"key":"3007_CR37","doi-asserted-by":"publisher","first-page":"5190","DOI":"10.1002\/anie.200800216","volume":"47","author":"M-SL Raddatz","year":"2008","unstructured":"Raddatz, M.-S. L. et al. Enrichment of cell-targeting and population-specific aptamers by fluorescence-activated cell sorting. Angew. Chem. Int. Ed. Engl. 47, 5190\u20135193 (2008).","journal-title":"Angew. Chem. Int. Ed. Engl."},{"key":"3007_CR38","first-page":"e64304","volume":"187","author":"M Nakhjavani","year":"2022","unstructured":"Nakhjavani, M. et al. A flow cytometry-based cell surface protein binding assay for assessing selectivity and specificity of an anticancer aptamer. J. Vis. Exp. 187, e64304 (2022).","journal-title":"J. Vis. Exp."},{"key":"3007_CR39","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","volume":"630","author":"J Abramson","year":"2024","unstructured":"Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493\u2013500 (2024).","journal-title":"Nature"},{"key":"3007_CR40","doi-asserted-by":"publisher","first-page":"4042","DOI":"10.1093\/bioinformatics\/btac426","volume":"38","author":"J Li","year":"2022","unstructured":"Li, J., Zhang, S., Zhang, D. & Chen, S.-J. Vfold-Pipeline: a web server for RNA 3D structure prediction from sequences. Bioinformatics 38, 4042\u20134043 (2022).","journal-title":"Bioinformatics"},{"key":"3007_CR41","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1002\/prot.70043","volume":"94","author":"RC Kretsch","year":"2026","unstructured":"Kretsch, R. C. et al. Functional relevance of CASP16 nucleic acid predictions as evaluated by structure providers. Proteins 94, 51\u201378 (2026).","journal-title":"Proteins"},{"key":"3007_CR42","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379\u2013423 (1948).","journal-title":"Bell Syst. Tech. J."},{"key":"3007_CR43","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1038\/s41596-020-0312-x","volume":"15","author":"Y Yan","year":"2020","unstructured":"Yan, Y., Tao, H., He, J. & Huang, S.-Y. The HDOCK server for integrated protein\u2013protein docking. Nat. Protocols 15, 1829\u20131852 (2020).","journal-title":"Nat. Protocols"},{"key":"3007_CR44","doi-asserted-by":"publisher","first-page":"e2112942118","DOI":"10.1073\/pnas.2112942118","volume":"118","author":"J Valero","year":"2021","unstructured":"Valero, J. et al. A serum-stable RNA aptamer specific for SARS-CoV-2 neutralizes viral entry. Proc. Natl Acad. Sci. USA 118, e2112942118 (2021).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"3007_CR45","doi-asserted-by":"publisher","first-page":"10266","DOI":"10.1002\/anie.202100225","volume":"60","author":"M Sun","year":"2021","unstructured":"Sun, M. et al. Aptamer blocking strategy inhibits SARS-CoV-2 virus infection. Angew. Chem. Int. Ed. Engl. 60, 10266\u201310272 (2021).","journal-title":"Angew. Chem. Int. Ed. Engl."},{"key":"3007_CR46","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-23154-3","volume":"8","author":"T Bartoschik","year":"2018","unstructured":"Bartoschik, T. et al. Near-native, site-specific and purification-free protein labeling for quantitative protein interaction analysis by MicroScale Thermophoresis. Sci. Rep. 8, 4977 (2018).","journal-title":"Sci. Rep."},{"key":"3007_CR47","doi-asserted-by":"publisher","first-page":"9895","DOI":"10.1021\/acs.analchem.0c01394","volume":"92","author":"Y Song","year":"2020","unstructured":"Song, Y. et al. Discovery of aptamers targeting the receptor-binding domain of the SARS-CoV-2 spike glycoprotein. Anal. Chem. 92, 9895\u20139900 (2020).","journal-title":"Anal. Chem."},{"key":"3007_CR48","doi-asserted-by":"publisher","first-page":"7267","DOI":"10.1093\/nar\/gkab574","volume":"49","author":"J Li","year":"2021","unstructured":"Li, J. et al. Diverse high-affinity DNA aptamers for wild-type and B.1.1.7 SARS-CoV-2 spike proteins from a pre-structured DNA library. Nucleic Acids Res. 49, 7267\u20137279 (2021).","journal-title":"Nucleic Acids Res."},{"key":"3007_CR49","doi-asserted-by":"publisher","first-page":"10273","DOI":"10.1002\/anie.202100345","volume":"60","author":"X Liu","year":"2021","unstructured":"Liu, X. et al. Neutralizing aptamers block S\/RBD\u2212ACE2 interactions and prevent host cell infection. Angew. Chem. Int. Ed. Engl. 60, 10273\u201310278 (2021).","journal-title":"Angew. Chem. Int. Ed. Engl."},{"key":"3007_CR50","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1038\/s41392-021-00649-6","volume":"6","author":"G Yang","year":"2021","unstructured":"Yang, G. et al. Identification of SARS-CoV-2-against aptamer with high neutralization activity by blocking the RBD domain of spike protein 1. Signal Transduct. Target Ther. 6, 227 (2021).","journal-title":"Signal Transduct. Target Ther."},{"key":"3007_CR51","doi-asserted-by":"crossref","unstructured":"Alves Ferreira-Bravo, I. & DeStefano, J. J. Xeno-nucleic acid (XNA) 2\u2032-fluoro-arabino nucleic acid (FANA) aptamers to the receptor-binding domain of SARS-CoV-2 S protein block ACE2 binding. Viruses 13, 1983 (2021).","DOI":"10.3390\/v13101983"},{"key":"3007_CR52","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1111\/cbdd.13978","volume":"99","author":"H Saify Nabiabad","year":"2022","unstructured":"Saify Nabiabad, H., Amini, M. & Demirdas, S. Specific delivering of RNAi using spike\u2019s aptamer-functionalized lipid nanoparticles for targeting SARS-CoV-2: a strong anti-Covid drug in a clinical case study. Chem. Biol. Drug Des. 99, 233\u2013246 (2022).","journal-title":"Chem. Biol. Drug Des."},{"key":"3007_CR53","doi-asserted-by":"publisher","DOI":"10.1186\/1748-7188-6-26","volume":"6","author":"R Lorenz","year":"2011","unstructured":"Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26 (2011).","journal-title":"Algorithms Mol. Biol."},{"key":"3007_CR54","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-017-3785-4","volume":"18","author":"C Guo","year":"2017","unstructured":"Guo, C. et al. Transversions have larger regulatory effects than transitions. BMC Genomics 18, 394 (2017).","journal-title":"BMC Genomics"},{"key":"3007_CR55","doi-asserted-by":"publisher","first-page":"21541","DOI":"10.1021\/jacs.1c08226","volume":"143","author":"M Sun","year":"2021","unstructured":"Sun, M. et al. Spherical neutralizing aptamer inhibits SARS-CoV-2 infection and suppresses mutational escape. J. Am. Chem. Soc. 143, 21541\u201321548 (2021).","journal-title":"J. Am. Chem. Soc."},{"key":"3007_CR56","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1038\/nrc.2017.36","volume":"17","author":"CV Dang","year":"2017","unstructured":"Dang, C. V., Reddy, E. P., Shokat, K. M. & Soucek, L. Drugging the \u2018undruggable\u2019 cancer targets. Nat. Rev. Cancer 17, 502\u2013508 (2017).","journal-title":"Nat. Rev. Cancer"},{"key":"3007_CR57","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202309639","volume":"11","author":"Y Wang","year":"2024","unstructured":"Wang, Y. et al. Antitumor effect of anti-c-Myc aptamer-based PROTAC for degradation of the c-Myc protein. Adv. Sci. 11, 2309639 (2024).","journal-title":"Adv. Sci."},{"key":"3007_CR58","unstructured":"He, J., Spokoyny, D., Neubig, G. & Berg-Kirkpatrick, T. Lagging inference networks and posterior collapse in variational autoencoders. In The Seventh International Conference on Learning Representations https:\/\/openreview.net\/pdf\/47f79f4015dbabc7f2eab6e432cddf975cf1c486.pdf (ICLR, 2019)."},{"key":"3007_CR59","unstructured":"Dieng, A. B., Kim, Y., Rush, A. M. & Blei, D. M. Avoiding latent variable collapse with generative skip models. In Proc. Twenty-Second International Conference on Artificial Intelligence and Statistics (eds Kamalika, C. & Masashi, S.) 2397\u20132405 (PMLR, 2019)."},{"key":"3007_CR60","doi-asserted-by":"publisher","first-page":"e1008736","DOI":"10.1371\/journal.pcbi.1008736","volume":"17","author":"A Hawkins-Hooker","year":"2021","unstructured":"Hawkins-Hooker, A. et al. Generating functional protein variants with variational autoencoders. PLoS Comput. Biol. 17, e1008736 (2021).","journal-title":"PLoS Comput. Biol."},{"key":"3007_CR61","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1038\/s41592-022-01585-1","volume":"19","author":"C Zhang","year":"2022","unstructured":"Zhang, C., Shine, M., Pyle, A. M. & Zhang, Y. US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nat. Methods 19, 1109\u20131115 (2022).","journal-title":"Nat. Methods"},{"key":"3007_CR62","doi-asserted-by":"publisher","first-page":"W521","DOI":"10.1093\/nar\/gkae241","volume":"52","author":"F Madeira","year":"2024","unstructured":"Madeira, F. et al. The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024. Nucleic Acids Res. 52, W521\u2013W525 (2024).","journal-title":"Nucleic Acids Res."},{"key":"3007_CR63","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1101\/gr.849004","volume":"14","author":"GE Crooks","year":"2004","unstructured":"Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188\u20131190 (2004).","journal-title":"Genome Res."},{"key":"3007_CR64","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-32007-7","volume":"13","author":"N Ferruz","year":"2022","unstructured":"Ferruz, N., Schmidt, S. & H\u00f6cker, B. ProtGPT2 is a deep unsupervised language model for protein design. Nat. Commun. 13, 4348 (2022).","journal-title":"Nat. Commun."},{"key":"3007_CR65","doi-asserted-by":"publisher","first-page":"eadl4000","DOI":"10.1126\/sciadv.adl4000","volume":"10","author":"B Ni","year":"2024","unstructured":"Ni, B., Kaplan, D. L. & Buehler, M. J. ForceGen: end-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024).","journal-title":"Sci. Adv."},{"key":"3007_CR66","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1038\/s41587-022-01618-2","volume":"41","author":"A Madani","year":"2023","unstructured":"Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41, 1099\u20131106 (2023).","journal-title":"Nat. Biotechnol."},{"key":"3007_CR67","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-56751-8","volume":"16","author":"Q Zhang","year":"2025","unstructured":"Zhang, Q. et al. Integrating protein language models and automatic biofoundry for enhanced protein evolution. Nat. Commun. 16, 1553 (2025).","journal-title":"Nat. Commun."},{"key":"3007_CR68","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1038\/s41592-023-01924-w","volume":"20","author":"A Singh","year":"2023","unstructured":"Singh, A. Protein language models guide directed antibody evolution. Nat. Methods 20, 785 (2023).","journal-title":"Nat. Methods"},{"key":"3007_CR69","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1038\/s41592-024-02523-z","volume":"22","author":"H Dalla-Torre","year":"2025","unstructured":"Dalla-Torre, H. et al. Nucleotide Transformer: building and evaluating robust foundation models for human genomics. Nat. Methods 22, 287\u2013297 (2025).","journal-title":"Nat. Methods"},{"key":"3007_CR70","unstructured":"Vaswani, A. et al. Attention is all you need. In Proc. 31st International Conference on Neural Information Processing Systems 6000\u20136010 (Curran Associates, 2017)."},{"key":"3007_CR71","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1038\/s42256-022-00532-1","volume":"4","author":"E Castro","year":"2022","unstructured":"Castro, E. et al. Transformer-based protein generation with regularized latent space optimization. Nat. Mach. Intell. 4, 840\u2013851 (2022).","journal-title":"Nat. Mach. Intell."},{"key":"3007_CR72","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1038\/s42256-022-00499-z","volume":"4","author":"N Ferruz","year":"2022","unstructured":"Ferruz, N. & H\u00f6cker, B. Controllable protein design with language models. Nat. Mach. Intell. 4, 521\u2013532 (2022).","journal-title":"Nat. Mach. Intell."},{"key":"3007_CR73","doi-asserted-by":"publisher","first-page":"7480","DOI":"10.1093\/nar\/gky583","volume":"46","author":"N Nguyen Quang","year":"2018","unstructured":"Nguyen Quang, N., Bouvier, C., Henriques, A., Lelandais, B. & Ducong\u00e9, F. Time-lapse imaging of molecular evolution by high-throughput sequencing. Nucleic Acids Res. 46, 7480\u20137494 (2018).","journal-title":"Nucleic Acids Res."},{"key":"3007_CR74","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-S1-S33","volume":"10","author":"CY Chan","year":"2009","unstructured":"Chan, C. Y. et al. A structural interpretation of the effect of GC-content on efficiency of RNA interference. BMC Bioinformatics 10, S33 (2009).","journal-title":"BMC Bioinformatics"},{"key":"3007_CR75","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1006\/jmbi.1999.2700","volume":"288","author":"DH Mathews","year":"1999","unstructured":"Mathews, D. H., Sabina, J., Zuker, M. & Turner, D. H. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288, 911\u2013940 (1999).","journal-title":"J. Mol. Biol."},{"key":"3007_CR76","doi-asserted-by":"publisher","first-page":"115118","DOI":"10.1016\/j.ab.2023.115118","volume":"669","author":"NA Molejon","year":"2023","unstructured":"Molejon, N. A. et al. Selection of G-rich ssDNA aptamers for the detection of enterotoxins of the cholera toxin family. Anal. Biochem. 669, 115118 (2023).","journal-title":"Anal. Biochem."},{"key":"3007_CR77","doi-asserted-by":"crossref","unstructured":"Melikishvili, M. et al. SELEX identifies high-affinity RNA targets for chromatin-binding proteins PARP1 and MeCP2. iScience 28, 113299 (2025).","DOI":"10.1016\/j.isci.2025.113299"},{"key":"3007_CR78","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1089\/nat.2011.0288","volume":"21","author":"WH Thiel","year":"2011","unstructured":"Thiel, W. H. et al. Nucleotide bias observed with a short SELEX RNA aptamer library. Nucleic Acid Ther. 21, 253\u2013263 (2011).","journal-title":"Nucleic Acid Ther."},{"key":"3007_CR79","doi-asserted-by":"publisher","unstructured":"Zhang, J. et al. CRISmers NGS data used and generated by GRAPE-LM (1.0). Zenodo https:\/\/doi.org\/10.5281\/zenodo.18005327 (2025).","DOI":"10.5281\/zenodo.18005327"}],"container-title":["Nature Biotechnology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41587-026-03007-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41587-026-03007-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41587-026-03007-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T10:02:52Z","timestamp":1770372172000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41587-026-03007-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,6]]},"references-count":79,"alternative-id":["3007"],"URL":"https:\/\/doi.org\/10.1038\/s41587-026-03007-5","relation":{},"ISSN":["1087-0156","1546-1696"],"issn-type":[{"value":"1087-0156","type":"print"},{"value":"1546-1696","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,6]]},"assertion":[{"value":"3 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}